- IEEE PERVASIVE COMPUTING Journal
- June 2010
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The Internet of Things (IoT) concept promises a world of networked and interconnected devices that provides relevant content to users. Recommender systems can find relevant content for users in IoT
environments, offering a user-adapted personalized experience.
Collaboration-based recommenders in IoT environments rely on
user-to-object, space-time interaction patterns. This extension of that
idea takes into account user location and interaction time to recommend
scattered, pervasive context-embedded networked objects. The authors
compare their proposed system to memory-based collaborative methods in
which user similarity is based on the ratings of previously rated items.
Their proof-of-concept implementation was used in a real-world scenario
involving 15 students interacting with 75 objects at Carlos III
University of Madrid.